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   "source": [
    "\n",
    "## Wide Array Declustering Distribution Representativity for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wide Array Declustering Data Distribution Representativity Plotting in Python with GeostatsPy\n",
    "\n",
    "Here's a simple workflow with some basic univariate statistics and distribution representativity. This should help you get started data declustering to address spatial sampling bias.\n",
    "\n",
    "#### Geostatistical Sampling Representativity\n",
    "\n",
    "In general, we should assume that all spatial data that we work with is biased.\n",
    "\n",
    "##### Source of Spatial Sampling Bias\n",
    "\n",
    "Data is collected to answer questions:\n",
    "* how far does the contaminant plume extend? – sample peripheries\n",
    "* where is the fault? – drill based on seismic interpretation\n",
    "* what is the highest mineral grade? – sample the best part\n",
    "* who far does the reservoir extend? – offset drilling\n",
    "and to maximize NPV directly:\n",
    "* maximize production rates\n",
    "\n",
    "**Random Sampling**: when every item in the population has a equal chance of being chosen. Selection of every item is independent of every other selection.\n",
    "Is random sampling sufficient for subsurface?  Is it available?\n",
    "* it is not usually available, would not be economic\n",
    "* data is collected answer questions\n",
    "    * how large is the reservoir, what is the thickest part of the reservoir \n",
    "* and wells are located to maximize future production\n",
    "    * dual purpose appraisal and injection / production wells!\n",
    "\n",
    "**Regular Sampling**: when samples are taken at regular intervals (equally spaced).  \n",
    "* less reliable than random sampling.\n",
    "* Warning: may resonate with some unsuspected environmental variable.\n",
    "\n",
    "What do we have?\n",
    "* we usually have biased, opportunity sampling \n",
    "* we must account for bias (debiasing will be discussed later)\n",
    "\n",
    "So if we were designing sampling for representativity of the sample set and resulting sample statistics, by theory we have 2 options, random sampling and regular sampling.\n",
    "\n",
    "* What would happen if you proposed random sampling in the Gulf of Mexico at $150M per well?\n",
    "\n",
    "We should not change current sampling methods as they result in best economics, we should address sampling bias in the data.\n",
    "\n",
    "Never use raw spatial data without access sampling bias / correcting.\n",
    "\n",
    "##### Mitigating Sampling Bias\n",
    "\n",
    "In this demonstration we will take a biased spatial sample data set and apply declustering using **GeostatsPy** functionality.\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data_biased.csv at https://git.io/fh0CW\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "from scipy import stats                   # summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")             # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('sample_data_biased.csv')     # load our data table (wrong name!)\n",
    "df = pd.read_csv(r'https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No error now! It worked, we loaded our file into our DataFrame called 'df'. But how do you really know that it worked? Visualizing the DataFrame would be useful and we already leard about these methods in this demo (https://git.io/fNgRW). \n",
    "\n",
    "We can preview the DataFrame by printing a slice or by utilizing the 'head' DataFrame member function (with a nice and clean format, see below). With the slice we could look at any subset of the data table and with the head command, add parameter 'n=13' to see the first 13 rows of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>100</td>\n",
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       "      <th>4</th>\n",
       "      <td>100</td>\n",
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       "      <td>0.113049</td>\n",
       "      <td>10.886001</td>\n",
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       "      <th>5</th>\n",
       "      <td>200</td>\n",
       "      <td>800</td>\n",
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       "      <td>0.154648</td>\n",
       "      <td>106.491795</td>\n",
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       "      <th>6</th>\n",
       "      <td>200</td>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>0.153113</td>\n",
       "      <td>140.976324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.126167</td>\n",
       "      <td>12.548074</td>\n",
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       "      <td>0.150961</td>\n",
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       "      <th>10</th>\n",
       "      <td>300</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.199227</td>\n",
       "      <td>1079.709291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>300</td>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>0.154220</td>\n",
       "      <td>179.491695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>300</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.137502</td>\n",
       "      <td>38.164911</td>\n",
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      "text/plain": [
       "      X    Y  Facies  Porosity         Perm\n",
       "0   100  900       1  0.115359     5.736104\n",
       "1   100  800       1  0.136425    17.211462\n",
       "2   100  600       1  0.135810    43.724752\n",
       "3   100  500       0  0.094414     1.609942\n",
       "4   100  100       0  0.113049    10.886001\n",
       "5   200  800       1  0.154648   106.491795\n",
       "6   200  700       1  0.153113   140.976324\n",
       "7   200  500       1  0.126167    12.548074\n",
       "8   200  400       0  0.094750     1.208561\n",
       "9   200  100       1  0.150961    44.687430\n",
       "10  300  800       1  0.199227  1079.709291\n",
       "11  300  700       1  0.154220   179.491695\n",
       "12  300  500       1  0.137502    38.164911"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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    }
   ],
   "source": [
    "#print(df.iloc[0:5,:])                   # display first 4 samples in the table as a preview\n",
    "df.head(n=13)                           # we could also use this command for a table preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 2 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), and permeability as Perm (mDarcy). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
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       "      <th>X</th>\n",
       "      <td>289.0</td>\n",
       "      <td>475.813149</td>\n",
       "      <td>254.277530</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>990.000000</td>\n",
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       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>289.0</td>\n",
       "      <td>529.692042</td>\n",
       "      <td>300.895374</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>819.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>289.0</td>\n",
       "      <td>0.813149</td>\n",
       "      <td>0.390468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>289.0</td>\n",
       "      <td>0.134744</td>\n",
       "      <td>0.037745</td>\n",
       "      <td>0.058548</td>\n",
       "      <td>0.106318</td>\n",
       "      <td>0.126167</td>\n",
       "      <td>0.154220</td>\n",
       "      <td>0.228790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>289.0</td>\n",
       "      <td>207.832368</td>\n",
       "      <td>559.359350</td>\n",
       "      <td>0.075819</td>\n",
       "      <td>3.634086</td>\n",
       "      <td>14.908970</td>\n",
       "      <td>71.454424</td>\n",
       "      <td>5308.842566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          count        mean         std       min         25%         50%  \\\n",
       "X         289.0  475.813149  254.277530  0.000000  300.000000  430.000000   \n",
       "Y         289.0  529.692042  300.895374  9.000000  269.000000  549.000000   \n",
       "Facies    289.0    0.813149    0.390468  0.000000    1.000000    1.000000   \n",
       "Porosity  289.0    0.134744    0.037745  0.058548    0.106318    0.126167   \n",
       "Perm      289.0  207.832368  559.359350  0.075819    3.634086   14.908970   \n",
       "\n",
       "                 75%          max  \n",
       "X         670.000000   990.000000  \n",
       "Y         819.000000   999.000000  \n",
       "Facies      1.000000     1.000000  \n",
       "Porosity    0.154220     0.228790  \n",
       "Perm       71.454424  5308.842566  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specify the Area of Interest\n",
    "\n",
    "It is natural to set the x and y coordinate and feature ranges manually. e.g. do you want your color bar to go from 0.05887 to 0.24230 exactly? Also, let's pick a color map for display. I heard that plasma is known to be friendly to the color blind as the color and intensity vary together (hope I got that right, it was an interesting Twitter conversation started by Matt Hall from Agile if I recall correctly). We will assume a study area of 0 to 1,000m in x and y and omit any data outside this area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0               # range of x values\n",
    "ymin = 0.0; ymax = 1000.0               # range of y values\n",
    "pormin = 0.05; pormax = 0.26;             # range of porosity values\n",
    "cmap = plt.cm.inferno                    # color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing Tabular Data with Location Maps¶ \n",
    "Let's try out locmap. This is a reimplementation of GSLIB's locmap program that uses matplotlib. I hope you find it simpler than matplotlib, if you want to get more advanced and build custom plots lock at the source. If you improve it, send me the new code. Any help is appreciated. To see the parameters, just type the command name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.GSLIB.locmap(df, xcol, ycol, vcol, xmin, xmax, ymin, ymax, vmin, vmax, title, xlabel, ylabel, vlabel, cmap, fig_name)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.locmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can populate the plotting parameters and visualize the porosity data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2c0b07e2b20>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.locmap(df,'X','Y','Porosity',xmin,xmax,ymin,ymax,pormin,pormax,'Well Data - Porosity','X(m)','Y(m)','Porosity (fraction)',cmap,'locmap_Porosity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look carefully, and you'll notice the the spatial samples are more dense in the high porosity regions and lower in the low porosity regions.  There is preferential sampling.  We cannot use the naive statistics to represent this region.  We have to correct for the clustering of the samples in the high porosity regions. \n",
    "\n",
    "### Cell-based Declustering\n",
    "\n",
    "Let's try cell declustering. We can interpret that we will want to minimize the declustering mean and that a cell size of between 100 - 200m is likely a good cell size, this is 'an ocular' estimate of the largest average spacing in the sparsely sampled regions.   \n",
    "\n",
    "Let's check out the declus program reimplimented from GSLIB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.declus(df, xcol, ycol, vcol, iminmax, noff, ncell, cmin, cmax)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.declus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now populate the parameters. We will run a very wide range of cell sizes, from 10m to 2,000m ('cmin' and 'cmax') and take the cell size that minimizes the declustered mean ('iminmax' = 1 minimize, and = 0 maximize). Multiple offsets (number of these is 'noff') uses multiple grid origins and averages the results to remove sensitivity to grid position.  The ncell is the number of cell sizes.\n",
    "\n",
    "The output from this program is:\n",
    "\n",
    "* wts - an array with the weigths for each data (they sum to the number of data, 1 indicates nominal weight)\n",
    "* cell_sizes - an array with the considered cell sizes\n",
    "* dmeans - de an "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 289 data with:\n",
      "   mean of      0.13474387540138408 \n",
      "   min and max  0.058547873 and 0.228790002\n",
      "   standard dev 0.03767982164385208 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>Wts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115359</td>\n",
       "      <td>5.736104</td>\n",
       "      <td>3.064286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>17.211462</td>\n",
       "      <td>1.076608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135810</td>\n",
       "      <td>43.724752</td>\n",
       "      <td>0.997239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.094414</td>\n",
       "      <td>1.609942</td>\n",
       "      <td>1.165119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.113049</td>\n",
       "      <td>10.886001</td>\n",
       "      <td>1.224164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y  Facies  Porosity       Perm       Wts\n",
       "0  100  900       1  0.115359   5.736104  3.064286\n",
       "1  100  800       1  0.136425  17.211462  1.076608\n",
       "2  100  600       1  0.135810  43.724752  0.997239\n",
       "3  100  500       0  0.094414   1.609942  1.165119\n",
       "4  100  100       0  0.113049  10.886001  1.224164"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wts, cell_sizes, dmeans = geostats.declus(df,'X','Y','Porosity',iminmax = 1, noff= 10, ncell=100,cmin=10,cmax=2000)\n",
    "df['Wts'] = wts                            # add weights to the sample data DataFrame\n",
    "df.head()                                  # preview to check the sample data DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the location map of the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2c0b08a54f0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.locmap(df,'X','Y','Wts',xmin,xmax,ymin,ymax,0.5,2.5,'Well Data Weights','X(m)','Y(m)','Weights',cmap,'locmap_Weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does it look correct?  See the weight varies with local sampling density?  \n",
    "\n",
    "Now let's add the distribution of the weights and the naive and declustered porosity distributions. You should see the histogram bars adjusted by the weights. Also note the change in the mean due to the weights. There is a significant change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porosity naive mean is 0.135.\n",
      "Porosity declustered mean is 0.121.\n",
      "Correction of 0.1002.\n",
      "\n",
      "Summary statistics of the declsutering weights:\n",
      "DescribeResult(nobs=289, minmax=(0.2819756671865989, 3.984325446814365), mean=0.9999999999999996, variance=0.40927076480664726, skewness=1.9395015754905447, kurtosis=4.287460624778004)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "GSLIB.locmap_st(df,'X','Y','Wts',xmin,xmax,ymin,ymax,0.0,2.0,'Declustering Weights','X (m)','Y (m)','Weights',cmap)\n",
    "\n",
    "plt.subplot(222)\n",
    "GSLIB.hist_st(df['Wts'],0.5,2.5,log=False,cumul=False,bins=20,weights=None,xlabel=\"Weights\",title=\"Declustering Weights\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "plt.subplot(223)\n",
    "GSLIB.hist_st(df['Porosity'],0.05,5000,log=False,cumul=False,bins=20,weights=None,xlabel=\"Porosity\",title=\"Naive Perm\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "plt.subplot(224)\n",
    "GSLIB.hist_st(df['Porosity'],0.05,0.25,log=False,cumul=False,bins=20,weights=df['Wts'],xlabel=\"Porosity\",title=\"Declustered Porosity\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "por_mean = np.average(df['Porosity'].values)\n",
    "por_dmean = np.average(df['Porosity'].values,weights=df['Wts'].values)\n",
    "\n",
    "print('Porosity naive mean is ' + str(round(por_mean,3))+'.')\n",
    "print('Porosity declustered mean is ' + str(round(por_dmean,3))+'.')\n",
    "cor = (por_mean-por_dmean)/por_mean\n",
    "print('Correction of ' + str(round(cor,4)) +'.')\n",
    "\n",
    "print('\\nSummary statistics of the declsutering weights:')\n",
    "print(stats.describe(wts))\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the plot of the declustered porosity mean vs. the declustering cell size over the 100 runs. At very small and very large cell size the declustered mean is the naive mean. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)\n",
    "plt.scatter(cell_sizes,dmeans, s=30, alpha = 0.2, edgecolors = \"black\", facecolors = 'red')\n",
    "plt.xlabel('Cell Size (m)')\n",
    "plt.ylabel('Declustered Porosity Mean (fraction)')\n",
    "plt.title('Declustered Porosity Mean vs. Cell Size')\n",
    "plt.plot([0,2000],[por_mean,por_mean],color = 'black')\n",
    "plt.plot([200,200],[0.10,0.16],color = 'black',linestyle='dashed')\n",
    "plt.text(300., 0.136, r'Naive Porosity Mean')\n",
    "plt.text(500., 0.118, r'Declustered Porosity Mean')\n",
    "plt.text(230., 0.154, r'Minimizing')\n",
    "plt.text(230., 0.150, r'Cell Size')\n",
    "plt.ylim(0.10,0.16)\n",
    "plt.xlim(0,2000)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell size that minimizes the declustered mean is about 200m (estimated from the figure). This makes sense given our previous observation of the data spacing. \n",
    "\n",
    "### Kriging-based Declustering\n",
    "\n",
    "Let's take the standard 2D kriging method and modify it\n",
    "\n",
    "* we will sum the weights assigned to each spatial data\n",
    "\n",
    "* calculate the weights as the normalized sum of the weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def declus_kriging(\n",
    "    df,\n",
    "    xcol,\n",
    "    ycol,\n",
    "    vcol,\n",
    "    tmin,\n",
    "    tmax,\n",
    "    nx,\n",
    "    xmn,\n",
    "    xsiz,\n",
    "    ny,\n",
    "    ymn,\n",
    "    ysiz,\n",
    "    nxdis,\n",
    "    nydis,\n",
    "    ndmin,\n",
    "    ndmax,\n",
    "    radius,\n",
    "    ktype,\n",
    "    skmean,\n",
    "    vario,\n",
    "):\n",
    "    \"\"\"GSLIB's KB2D program (Deutsch and Journel, 1998) converted from the\n",
    "    original Fortran to Python by Michael Pyrcz, the University of Texas at\n",
    "    Austin (Jan, 2019).\n",
    "    :param df: pandas DataFrame with the spatial data\n",
    "    :param xcol: name of the x coordinate column\n",
    "    :param ycol: name of the y coordinate column\n",
    "    :param vcol: name of the property column\n",
    "    :param tmin: property trimming limit\n",
    "    :param tmax: property trimming limit\n",
    "    :param nx: definition of the grid system (x axis)\n",
    "    :param xmn: definition of the grid system (x axis)\n",
    "    :param xsiz: definition of the grid system (x axis)\n",
    "    :param ny: definition of the grid system (y axis)\n",
    "    :param ymn: definition of the grid system (y axis)\n",
    "    :param ysiz: definition of the grid system (y axis)\n",
    "    :param nxdis: number of discretization points for a block\n",
    "    :param nydis: number of discretization points for a block\n",
    "    :param ndmin: minimum number of data points to use for kriging a block\n",
    "    :param ndmax: maximum number of data points to use for kriging a block\n",
    "    :param radius: maximum isotropic search radius\n",
    "    :param ktype:\n",
    "    :param skmean:\n",
    "    :param vario:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    \n",
    "# Constants\n",
    "    UNEST = -999.\n",
    "    EPSLON = 1.0e-10\n",
    "    VERSION = 2.907\n",
    "    first = True\n",
    "    PMX = 9999.0    \n",
    "    MAXSAM = ndmax + 1\n",
    "    MAXDIS = nxdis * nydis\n",
    "    MAXKD = MAXSAM + 1\n",
    "    MAXKRG = MAXKD * MAXKD\n",
    "    \n",
    "# load the variogram\n",
    "    nst = vario['nst']\n",
    "    cc = np.zeros(nst); aa = np.zeros(nst); it = np.zeros(nst)\n",
    "    ang = np.zeros(nst); anis = np.zeros(nst)\n",
    "    \n",
    "    c0 = vario['nug']; \n",
    "    cc[0] = vario['cc1']; it[0] = vario['it1']; ang[0] = vario['azi1']; \n",
    "    aa[0] = vario['hmaj1']; anis[0] = vario['hmin1']/vario['hmaj1'];\n",
    "    if nst == 2:\n",
    "        cc[1] = vario['cc2']; it[1] = vario['it2']; ang[1] = vario['azi2']; \n",
    "        aa[1] = vario['hmaj2']; anis[1] = vario['hmin2']/vario['hmaj2'];\n",
    "    \n",
    "# Allocate the needed memory:   \n",
    "    xdb = np.zeros(MAXDIS)\n",
    "    ydb = np.zeros(MAXDIS)\n",
    "    xa = np.zeros(MAXSAM)\n",
    "    ya = np.zeros(MAXSAM)\n",
    "    vra = np.zeros(MAXSAM)\n",
    "    dist = np.zeros(MAXSAM)\n",
    "    nums = np.zeros(MAXSAM)\n",
    "    r = np.zeros(MAXKD)\n",
    "    rr = np.zeros(MAXKD)\n",
    "    s = np.zeros(MAXKD)\n",
    "    a = np.zeros(MAXKRG)\n",
    "    kmap = np.zeros((nx,ny))\n",
    "    vmap = np.zeros((nx,ny))\n",
    "\n",
    "# Load the data\n",
    "    df_extract = df.loc[(df[vcol] >= tmin) & (df[vcol] <= tmax)]    # trim values outside tmin and tmax\n",
    "    nd = len(df_extract)\n",
    "    ndmax = min(ndmax,nd)\n",
    "    x = df_extract[xcol].values\n",
    "    y = df_extract[ycol].values\n",
    "    vr = df_extract[vcol].values\n",
    "    \n",
    "# Make a KDTree for fast search of nearest neighbours   \n",
    "    dp = list((y[i], x[i]) for i in range(0,nd))\n",
    "    data_locs = np.column_stack((y,x))\n",
    "    tree = sp.cKDTree(data_locs, leafsize=16, compact_nodes=True, copy_data=False, balanced_tree=True)\n",
    "\n",
    "# Summary statistics for the data after trimming\n",
    "    avg = vr.mean()\n",
    "    stdev = vr.std()\n",
    "    ss = stdev**2.0\n",
    "    vrmin = vr.min()\n",
    "    vrmax = vr.max()\n",
    "    \n",
    "# Track the sum of weights for declustering\n",
    "    sum_wts = np.zeros(nd)\n",
    "\n",
    "# Set up the discretization points per block.  Figure out how many\n",
    "# are needed, the spacing, and fill the xdb and ydb arrays with the\n",
    "# offsets relative to the block center (this only gets done once):\n",
    "    ndb  = nxdis * nydis\n",
    "    if ndb > MAXDIS: \n",
    "        print('ERROR KB2D: Too many discretization points ')\n",
    "        print('            Increase MAXDIS or lower n[xy]dis')\n",
    "        return kmap\n",
    "    xdis = xsiz  / max(float(nxdis),1.0)\n",
    "    ydis = ysiz  / max(float(nydis),1.0)\n",
    "    xloc = -0.5*(xsiz+xdis)\n",
    "    i    = -1   # accounting for 0 as lowest index\n",
    "    for ix in range(0,nxdis):       \n",
    "        xloc = xloc + xdis\n",
    "        yloc = -0.5*(ysiz+ydis)\n",
    "        for iy in range(0,nydis): \n",
    "            yloc = yloc + ydis\n",
    "            i = i+1\n",
    "            xdb[i] = xloc\n",
    "            ydb[i] = yloc\n",
    "\n",
    "# Initialize accumulators:\n",
    "    cbb  = 0.0\n",
    "    rad2 = radius*radius\n",
    "\n",
    "# Calculate Block Covariance. Check for point kriging.\n",
    "    rotmat, maxcov = geostats.setup_rotmat(c0,nst,it,cc,ang,PMX)\n",
    "    cov = geostats.cova2(xdb[0],ydb[0],xdb[0],ydb[0],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "# Keep this value to use for the unbiasedness constraint:\n",
    "    unbias = cov\n",
    "    first  = False\n",
    "    if ndb <= 1:\n",
    "        cbb = cov\n",
    "    else:\n",
    "        for i in range(0,ndb): \n",
    "            for j in range(0,ndb): \n",
    "                cov = cova2(xdb[i],ydb[i],xdb[j],ydb[j],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "            if i == j: \n",
    "                cov = cov - c0\n",
    "            cbb = cbb + cov\n",
    "        cbb = cbb/real(ndb*ndb)\n",
    "\n",
    "# MAIN LOOP OVER ALL THE BLOCKS IN THE GRID:\n",
    "    nk = 0\n",
    "    ak = 0.0\n",
    "    vk = 0.0\n",
    "    for iy in range(0,ny):\n",
    "        yloc = ymn + (iy-0)*ysiz  \n",
    "        for ix in range(0,nx):\n",
    "            xloc = xmn + (ix-0)*xsiz\n",
    "            current_node = (yloc,xloc)\n",
    "        \n",
    "# Find the nearest samples within each octant: First initialize\n",
    "# the counter arrays:\n",
    "            na = -1   # accounting for 0 as first index\n",
    "            dist.fill(1.0e+20)\n",
    "            nums.fill(-1)\n",
    "            dist, nums = tree.query(current_node,ndmax) # use kd tree for fast nearest data search\n",
    "            # remove any data outside search radius\n",
    "            na = len(dist)\n",
    "            nums = nums[dist<radius]\n",
    "            dist = dist[dist<radius] \n",
    "            na = len(dist)        \n",
    "\n",
    "# Is there enough samples?\n",
    "            if na + 1 < ndmin:   # accounting for min index of 0\n",
    "                est  = UNEST\n",
    "                estv = UNEST\n",
    "                print('UNEST at ' + str(ix) + ',' + str(iy))\n",
    "            else:\n",
    "\n",
    "# Put coordinates and values of neighborhood samples into xa,ya,vra:\n",
    "                for ia in range(0,na):\n",
    "                    jj = int(nums[ia])\n",
    "                    xa[ia]  = x[jj]\n",
    "                    ya[ia]  = y[jj]\n",
    "                    vra[ia] = vr[jj]\n",
    "                    \n",
    "# Handle the situation of only one sample:\n",
    "                if na == 0:  # accounting for min index of 0 - one sample case na = 0\n",
    "                    cb1 = geostats.cova2(xa[0],ya[0],xa[0],ya[0],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                    xx  = xa[0] - xloc\n",
    "                    yy  = ya[0] - yloc\n",
    "\n",
    "# Establish Right Hand Side Covariance:\n",
    "                    if ndb <= 1:\n",
    "                        cb = geostats.cova2(xx,yy,xdb[0],ydb[0],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                    else:\n",
    "                        cb  = 0.0\n",
    "                        for i in range(0,ndb):                  \n",
    "                            cb = cb + cova2(xx,yy,xdb[i],ydb[i],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                            dx = xx - xdb(i)\n",
    "                            dy = yy - ydb(i)\n",
    "                            if (dx*dx+dy*dy) < EPSLON:\n",
    "                                cb = cb - c0\n",
    "                            cb = cb / real(ndb)\n",
    "                    if ktype == 0:\n",
    "                        s[0] = cb/cbb\n",
    "                        est  = s[0]*vra[0] + (1.0-s[0])*skmean\n",
    "                        estv = cbb - s[0] * cb\n",
    "                    else:\n",
    "                        est  = vra[0]\n",
    "                        estv = cbb - 2.0*cb + cb1\n",
    "\n",
    "# sum the kriging weights\n",
    "                   # print(nums)\n",
    "                    #jj = int(nums[0])\n",
    "                    #sum_wts[jj] = sum_wts[jj] + s[0]   \n",
    "\n",
    "                        \n",
    "                else:\n",
    "\n",
    "# Solve the Kriging System with more than one sample:\n",
    "                    neq = na + ktype # accounting for first index of 0\n",
    "#                    print('NEQ' + str(neq))\n",
    "                    nn  = (neq + 1)*neq/2\n",
    "\n",
    "# Set up kriging matrices:\n",
    "                    iin=-1 # accounting for first index of 0\n",
    "                    for j in range(0,na):\n",
    "\n",
    "# Establish Left Hand Side Covariance Matrix:\n",
    "                        for i in range(0,na):  # was j - want full matrix                    \n",
    "                            iin = iin + 1\n",
    "                            a[iin] = geostats.cova2(xa[i],ya[i],xa[j],ya[j],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov) \n",
    "                        if ktype == 1:\n",
    "                            iin = iin + 1\n",
    "                            a[iin] = unbias\n",
    "                        xx = xa[j] - xloc\n",
    "                        yy = ya[j] - yloc\n",
    "\n",
    "# Establish Right Hand Side Covariance:\n",
    "                        if ndb <= 1:\n",
    "                            cb = geostats.cova2(xx,yy,xdb[0],ydb[0],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                        else:\n",
    "                            cb  = 0.0\n",
    "                            for j1 in range(0,ndb):    \n",
    "                                cb = cb + cova2(xx,yy,xdb[j1],ydb[j1],nst,c0,PMX,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "                                dx = xx - xdb[j1]\n",
    "                                dy = yy - ydb[j1]\n",
    "                                if (dx*dx+dy*dy) < EPSLON:\n",
    "                                    cb = cb - c0\n",
    "                            cb = cb / real(ndb)\n",
    "                        r[j]  = cb\n",
    "                        rr[j] = r[j]\n",
    "\n",
    "# Set the unbiasedness constraint:\n",
    "                    if ktype == 1:\n",
    "                        for i in range(0,na):\n",
    "                            iin = iin + 1\n",
    "                            a[iin] = unbias\n",
    "                        iin      = iin + 1\n",
    "                        a[iin]   = 0.0\n",
    "                        r[neq-1]  = unbias\n",
    "                        rr[neq-1] = r[neq]\n",
    "\n",
    "# Solve the Kriging System:\n",
    "#                    print('NDB' + str(ndb))\n",
    "#                    print('NEQ' + str(neq) + ' Left' + str(a) + ' Right' + str(r))\n",
    "#                    stop\n",
    "                    s = geostats.ksol_numpy(neq,a,r)\n",
    "                    ising = 0 # need to figure this out\n",
    "#                    print('weights' + str(s))\n",
    "#                    stop\n",
    "                \n",
    "            \n",
    "# Write a warning if the matrix is singular:\n",
    "                    if ising != 0:\n",
    "                        print('WARNING KB2D: singular matrix')\n",
    "                        print('              for block' + str(ix) + ',' + str(iy)+ ' ')\n",
    "                        est  = UNEST\n",
    "                        estv = UNEST\n",
    "                    else:\n",
    "\n",
    "# Compute the estimate and the kriging variance:\n",
    "                        est  = 0.0\n",
    "                        estv = cbb\n",
    "                        sumw = 0.0\n",
    "                        if ktype == 1: \n",
    "                            estv = estv - (s[na])*unbias\n",
    "                        for i in range(0,na):                          \n",
    "                            sumw = sumw + s[i]\n",
    "                            est  = est  + s[i]*vra[i]\n",
    "                            estv = estv - s[i]*rr[i]\n",
    "\n",
    "# sum the kriging weights\n",
    "                            jj = int(nums[i])\n",
    "                            sum_wts[jj] = sum_wts[jj] + s[i]\n",
    "                            \n",
    "                        if ktype == 0: \n",
    "                            est = est + (1.0-sumw)*skmean\n",
    "            kmap[ny-iy-1,ix] = est\n",
    "            vmap[ny-iy-1,ix] = estv\n",
    "            if est > UNEST:\n",
    "                nk = nk + 1\n",
    "                ak = ak + est\n",
    "                vk = vk + est*est\n",
    "\n",
    "# END OF MAIN LOOP OVER ALL THE BLOCKS:\n",
    "\n",
    "    if nk >= 1:\n",
    "        ak = ak / float(nk)\n",
    "        vk = vk/float(nk) - ak*ak\n",
    "        print('  Estimated   ' + str(nk) + ' blocks ')\n",
    "        print('      average   ' + str(ak) + '  variance  ' + str(vk))\n",
    "\n",
    "# standardize the kriging weights\n",
    "    sum_sum_wts = np.sum(sum_wts)\n",
    "    if sum_sum_wts <= 0.0:\n",
    "        sum_wts = np.ones(nd)\n",
    "    else:\n",
    "        sum_wts = sum_wts/sum_sum_wts*nd\n",
    "        \n",
    "    return sum_wts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assume a reasoanble grid for kriging\n",
    "\n",
    "* sufficient resolution to capture the spatial context of the data\n",
    "\n",
    "* higher resolution results in more precision in the declustering weights\n",
    "\n",
    "We will also assume simple kriging parameters.\n",
    "\n",
    "* we want to limit the search to the range of correlation for computation efficiency\n",
    "\n",
    "We will also assume a variogram\n",
    "\n",
    "* very large range will result in equal weights\n",
    "\n",
    "* no spatial correlation will result in equal weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set grid parameters\n",
    "xmin = 0.0; xmax = 1000.0               # range of x values\n",
    "ymin = 0.0; ymax = 1000.0               # range of y values\n",
    "xsiz = 10; ysiz = 10                    # cell size\n",
    "nx = 100; ny = 100                      # number of cells\n",
    "xmn = 5; ymn = 5                        # grid origin, location center of lower left cell\n",
    "\n",
    "# set kriging parameters\n",
    "nxdis = 1; nydis = 1                       # block kriging discretizations, 1 for point kriging\n",
    "ndmin = 0; ndmax = 10                      # minimum and maximum data for kriging \n",
    "radius = 100                               # maximum search distance\n",
    "ktype = 0                                  # kriging type, 0 - simple, 1 - ordinary\n",
    "ivtype = 0                                 # variable type, 0 - categorical, 1 - continuous\n",
    "tmin = -999; tmax = 999;                   # data trimming limits\n",
    "skmean = 0.0\n",
    "\n",
    "# define a variogram model\n",
    "vario = GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=100,hmin1=100) # porosity sand variogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the new kriging weight-based declustering method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Estimated   10000 blocks \n",
      "      average   0.08916482425663337  variance  0.001716118320222105\n"
     ]
    }
   ],
   "source": [
    "import numpy.linalg as linalg  # for linear algebra\n",
    "import scipy.spatial as sp  # for fast nearest neighbor search\n",
    "from numba import jit  # for numerical speed up\n",
    "from statsmodels.stats.weightstats import DescrStatsW\n",
    "wts_kriging = declus_kriging(df,'X','Y','Porosity',tmin,tmax,nx,xmn,xsiz,ny,ymn,ysiz,nxdis,nydis,\n",
    "         ndmin,ndmax,radius,ktype,skmean,vario)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the weights to the original DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>Wts</th>\n",
       "      <th>Wts_kriging</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115359</td>\n",
       "      <td>5.736104</td>\n",
       "      <td>3.064286</td>\n",
       "      <td>2.057207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>17.211462</td>\n",
       "      <td>1.076608</td>\n",
       "      <td>1.542487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135810</td>\n",
       "      <td>43.724752</td>\n",
       "      <td>0.997239</td>\n",
       "      <td>1.170248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.094414</td>\n",
       "      <td>1.609942</td>\n",
       "      <td>1.165119</td>\n",
       "      <td>1.938369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.113049</td>\n",
       "      <td>10.886001</td>\n",
       "      <td>1.224164</td>\n",
       "      <td>1.964978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y  Facies  Porosity       Perm       Wts  Wts_kriging\n",
       "0  100  900       1  0.115359   5.736104  3.064286     2.057207\n",
       "1  100  800       1  0.136425  17.211462  1.076608     1.542487\n",
       "2  100  600       1  0.135810  43.724752  0.997239     1.170248\n",
       "3  100  500       0  0.094414   1.609942  1.165119     1.938369\n",
       "4  100  100       0  0.113049  10.886001  1.224164     1.964978"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Wts_kriging'] = wts_kriging                           # add weights to the sample data DataFrame\n",
    "df.head()                                  # preview to check the sample data DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's add the distribution of the weights and the naive and declustered porosity distributions. You should see the histogram bars adjusted by the weights. Also note the change in the mean due to the weights. There is a significant change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porosity naive mean is 0.135.\n",
      "Porosity declustered mean is 0.125.\n",
      "Correction of 0.0742.\n",
      "\n",
      "Summary statistics of the declustering weights:\n",
      "DescribeResult(nobs=289, minmax=(0.2819756671865989, 3.984325446814365), mean=0.9999999999999996, variance=0.40927076480664726, skewness=1.9395015754905447, kurtosis=4.287460624778004)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "GSLIB.locmap_st(df,'X','Y','Wts_kriging',xmin,xmax,ymin,ymax,0.0,2.0,'Kriging-based Declustering Weights','X (m)','Y (m)','Weights',cmap)\n",
    "\n",
    "plt.subplot(222)\n",
    "GSLIB.hist_st(df['Wts_kriging'],0.5,2.5,log=False,cumul=False,bins=20,weights=None,xlabel=\"Kriging-based Weights\",title=\"Declustering Weights\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "plt.subplot(223)\n",
    "GSLIB.hist_st(df['Porosity'],0.05,0.25,log=False,cumul=False,bins=20,weights=None,xlabel=\"Porosity\",title=\"Naive Porosity\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "plt.subplot(224)\n",
    "GSLIB.hist_st(df['Porosity'],0.05,0.25,log=False,cumul=False,bins=20,weights=df['Wts_kriging'],xlabel=\"Porosity\",title=\"Kriging Declustered Porosity\")\n",
    "plt.ylim(0.0,60)\n",
    "\n",
    "por_mean = np.average(df['Porosity'].values)\n",
    "por_dmean = np.average(df['Porosity'].values,weights=df['Wts_kriging'].values)\n",
    "\n",
    "print('Porosity naive mean is ' + str(round(por_mean,3))+'.')\n",
    "print('Porosity declustered mean is ' + str(round(por_dmean,3))+'.')\n",
    "cor = (por_mean-por_dmean)/por_mean\n",
    "print('Correction of ' + str(round(cor,4)) +'.')\n",
    "\n",
    "print('\\nSummary statistics of the declustering weights:')\n",
    "print(stats.describe(wts))\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These declutering weights integrate:\n",
    "\n",
    "* the spatial arrangement of the data\n",
    "\n",
    "* the spatial continuity\n",
    "\n",
    "* the study area of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of declustering to correct for sampling bias. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays and many other workflows availble at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy.\n",
    "\n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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